Mammalian PERIOD2 regulates H2A.Z incorporation in chromatin to orchestrate circadian negative feedback

Epigenetic control of circadian clocks by environmental signals

Circadian clocks have evolved to enable organisms to respond to daily environmental changes. Maintaining a robust circadian rhythm under various perturbations and stresses is essential for the fitness of an organism. In the core circadian oscillator conserved in eukaryotes (from fungi to mammals), a negative feedback loop based on both transcription and translation drives circadian rhythms. The expression of circadian clock genes depends both on the binding of transcription activators at the promoter and on the chromatin state of the clock genes, and epigenetic modifications of chromatin are crucial for transcriptional regulation of circadian clock genes. Herein we review current knowledge of epigenetic regulation of circadian clock mechanisms and discuss how environmental cues can control clock gene expression by affecting chromatin states.

Proteomic insights into circadian transcription regulation: novel E-box interactors revealed by proximity labeling

Circadian clocks (∼24 h) are responsible for daily physiological, metabolic, and behavioral changes. Central to these oscillations is the regulation of gene transcription. Previous research has identified clock protein complexes that interact with the transcriptional machinery to orchestrate circadian transcription, but technological constraints have limited the identification of de novo proteins. Here we use a novel genomic locus-specific quantitative proteomics approach to provide a new perspective on time of day-dependent protein binding at a critical chromatin locus involved in circadian transcription: the E-box. Using proximity labeling proteomics at the E-box of the clock-controlled Dbp gene in mouse fibroblasts, we identified 69 proteins at this locus at the time of BMAL1 binding. This method successfully enriched BMAL1 as well as HDAC3 and HISTONE H2A.V/Z, known circadian regulators. New E-box proteins include the MINK1 kinase and the transporters XPO7 and APPL1, whose depletion in human U-2 OS cells results in disrupted circadian rhythms, suggesting a role in the circadian transcriptional machinery. Overall, our approach uncovers novel circadian modulators and provides a new strategy to obtain a complete temporal picture of circadian transcriptional regulation.

Neuronal Progenitors Suffer Genotoxic Stress in the Drosophila Clock Mutant per0

The physiological role and the molecular architecture of the circadian clock in fully developed organisms are well established. Yet, we have a limited understanding of the function of the clock during ontogenesis. We have used a null mutant (per0) of the clock gene period (per) in Drosophila melanogaster to ask whether PER may play a role during normal brain development. In third-instar larvae, we have observed that the absence of functional per results in increased genotoxic stress compared to wild-type controls. We have detected increased double-strand DNA breaks in the central nervous system and chromosome aberrations in dividing neuronal precursor cells. We have demonstrated that reactive oxygen species (ROS) are causal to the genotoxic effect and that expression of PER in glia is necessary and sufficient to suppress such a phenotype. Finally, we have shown that the absence of PER may result in less condensed chromatin, which contributes to DNA damage.

Polyploidy Promotes Hypertranscription, Apoptosis Resistance, and Ciliogenesis in Cancer Cells and Mesenchymal Stem Cells of Various Origins: Comparative Transcriptome In Silico Study

Mesenchymal stem cells (MSC) attract an increasing amount of attention due to their unique therapeutic properties. Yet, MSC can undergo undesirable genetic and epigenetic changes during their propagation in vitro. In this study, we investigated whether polyploidy can compromise MSC oncological safety and therapeutic properties. For this purpose, we compared the impact of polyploidy on the transcriptome of cancer cells and MSC of various origins (bone marrow, placenta, and heart). First, we identified genes that are consistently ploidy-induced or ploidy-repressed through all comparisons. Then, we selected the master regulators using the protein interaction enrichment analysis (PIEA). The obtained ploidy-related gene signatures were verified using the data gained from polyploid and diploid populations of early cardiomyocytes (CARD) originating from iPSC. The multistep bioinformatic analysis applied to the cancer cells, MSC, and CARD indicated that polyploidy plays a pivotal role in driving the cell into hypertranscription. It was evident from the upregulation of gene modules implicated in housekeeping functions, stemness, unicellularity, DNA repair, and chromatin opening by means of histone acetylation operating via DNA damage associated with the NUA4/TIP60 complex. These features were complemented by the activation of the pathways implicated in centrosome maintenance and ciliogenesis and by the impairment of the pathways related to apoptosis, the circadian clock, and immunity. Overall, our findings suggest that, although polyploidy does not induce oncologic transformation of MSC, it might compromise their therapeutic properties because of global epigenetic changes and alterations in fundamental biological processes. The obtained results can contribute to the development and implementation of approaches enhancing the therapeutic properties of MSC by removing polyploid cells from the cell population.

Detecting abnormal cell behaviors from dry mass time series

The prediction of pathological changes on single cell behaviour is a challenging task for deep learning models. Indeed, in self-supervised learning methods, no prior labels are used for the training and all of the information for event predictions are extracted from the data themselves. We present here a novel self-supervised learning model for the detection of anomalies in a given cell population, StArDusTS. Cells are monitored over time, and analysed to extract time-series of dry mass values. We assessed its performances on different cell lines, showing a precision of 96% in the automatic detection of anomalies. Additionally, anomaly detection was also associated with cell measurement errors inherent to the acquisition or analysis pipelines, leading to an improvement of the upstream methods for feature extraction. Our results pave the way to novel architectures for the continuous monitoring of cell cultures in applied research or bioproduction applications, and for the prediction of pathological cellular changes.